This paper investigates several optimum graph-cut techniques for pruning binary partition trees (BPTs) and their usefulness for the low-level processing of polarimetric synthetic aperture radar (PolSAR) images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the tree with a specific feature and in applying a graph cut called pruning to extract a partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Through the objective evaluation of the resulting partitions by means of precision-and-recall-for-boundaries curves, the best pruning technique is identified, and the influence of the tree construction on the performances is assessed.

Inferring gene regulatory networks from expression data is a very difficult problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that the different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this paper, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because the are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method ScaleLSum.

Background:
In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.
Results:
Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.
Conclusions:
The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

This paper investigates several pruning techniques applied on Binary Partition Trees (BPTs) and their usefulness for low-level processing of PolSAR images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the tree with a specific feature and in applying a graph-cut called pruning to extract a partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Initial experiments are also reported on the use of Minkowski norms in the definition of the optimization criterion.

In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure, which succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Hence, the BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region based perspective. For each region represented in the BPT, spatial and spectral descriptors are computed and the likelihood that they correspond to an instantiation of the object of interest is evaluated. Experimental results demonstrate the good performances of this BPT-based approach. (C) 2015 Elsevier B.V. All rights reserved.

The High Efficiency Video Coding standard (HEVC) supports a total of 35 intra prediction modes which aim at reducing spatial redundancy by exploiting pixel correlation within a local neighborhood. In this paper, we show that spatial correlation remains after intra prediction, leading to high energy prediction residues. We propose a novel scheme for encoding the prediction residues using a Mode Dependent Vector Quantization (MDVQ) which aims at reducing the redundancy in residual domain. The MDVQ codebook is optimized in a rate-distortion (RD) sense. Experimental results show that the codebook can be independent of the quantization parameter (QP) with no loss in terms of coding efficiency. A bitrate reduction of 1.1% on average compared to HEVC can be achieved, while further tests indicate that codebook adaptivity could substantially improve the performance.

The segmentation of remotely sensed images acquired over tropical forests is of great interest for numerous ecological applications, such as forest inventories or conservation and management of ecosystems, for which species classification techniques and estimation of the number of individuals are highly valuable inputs. In this paper, we propose a method for hyperspectral image segmentation, based on the binary partition tree (BPT) algorithm, and we apply it to two sites located in Hawaiian and Panamean tropical rainforests. Different strategies combining spatial and spectral dimensionality reduction are compared prior to the construction of the BPT. Various superpixel generation methods including watershed transformation and mean shift clustering are applied to decrease spatial dimensionality and provide an initial segmentation map. Principal component analysis is performed to reduce the spectral dimensionality and different combinations of principal components are compared. A non-parametric region model based on histograms, combined with the diffusion distance to merge regions, is used to build the BPT. An adapted pruning strategy based on the size discontinuity of the merging regions is proposed and compared with an already existing pruning strategy. Finally, a set of criteria to assess the quality of the tree segmentation is introduced. The proposed method correctly segmented up to 68% of the tree crowns and produced reasonable patterns of the segmented landscapes.

Many computer vision applications involve algorithms that can be decomposed in two main steps. In a first step, events or objects are detected and, in a second step, detections are assigned to classes. Examples of such

This paper discusses the interest of Binary Partition Trees (BPTs) and the usefulness of graph cuts for low-level processing of PolSAR images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for many applications including filtering, segmentation, classification and object detection. Many processing strategies consist in populating the tree with a specific feature and in applying a graph-cut called pruning. Different graph-cuts are discussed and analyzed in the context of PolSAR images for speckle filtering and segmentation.

In the field of computational biology, microarryas are used to measure the activity of thousands of genes at once and create a global picture of cellular function. Microarrays allow scientists to analyze expression of many genes in a single experiment quickly and eficiently. Even if microarrays are a consolidated research technology nowadays and the trends in high-throughput data analysis are shifting towards new technologies like Next Generation Sequencing (NGS), an optimum method for sample classification has not been found yet.
Microarray classification is a complicated task, not only due to the high dimensionality of the feature set, but also to an apparent lack of data structure. This characteristic limits the applicability of processing techniques, such as wavelet filtering or other filtering techniques that take advantage of known structural relation. On the other hand, it is well known that genes are not expressed independently from other each other: genes have a high interdependence related to the involved regulating biological process.
This thesis aims to improve the current state of the art in microarray classification and to contribute to understand how signal processing techniques can be developed and applied to analyze microarray data. The goal of building a classification framework needs an exploratory work in which algorithms are constantly tried and adapted to the analyzed data. The developed algorithms and classification frameworks in this thesis tackle the problem with two essential building blocks. The first one deals with the lack of a priori structure by inferring a data-driven structure with unsupervised hierarchical clustering tools. The second key element is a proper feature selection tool to produce a precise classifier as an output and to reduce the overfitting risk.
The main focus in this thesis is the binary data classification, field in which we obtained relevant improvements to the state of the art. The first key element is the data-driven structure, obtained by modifying hierarchical clustering algorithms derived from the Treelets algorithm from the literature. Several alternatives to the original reference algorithm have been tested, changing either the similarity metric to merge the feature or the way two feature are merged. Moreover, the possibility to include external sources of information from publicly available biological knowledge and ontologies to improve the structure generation has been studied too. About the feature selection, two alternative approaches have been studied: the first one is a modification of the IFFS algorithm as a wrapper feature selection, while the second approach involved an ensemble learning focus. To obtain good results, the IFFS algorithm has been adapted to the data characteristics by introducing new elements to the selection process like a reliability measure and a scoring system to better select the best feature at each iteration. The second feature selection approach is based on Ensemble learning, taking advantage of the microarryas feature abundance to implement a different selection scheme. New algorithms have been studied in this field, improving state of the art algorithms to the microarray data characteristic of small sample and high feature numbers.
In addition to the binary classification problem, the multiclass case has been addressed too. A new algorithm combining multiple binary classifiers has been evaluated, exploiting the redundancy offered by multiple classifiers to obtain better predictions. All the studied algorithm throughout this thesis have been evaluated using high quality publicly available data, following established testing protocols from the literature to offer a proper benchmarking with the state of the art. Whenever possible, multiple Monte Carlo simulations have been performed to increase the robustness of the obtained results.

This paper deals with the processing of polarimetric synthetic aperture radar (SAR) time series. Different approaches to deal with the temporal dimension of the data are considered, which are derived from different target characterizations in this dimension. These approaches are the basis for defining two different binary partition tree (BPT) structures that are employed for SAR polarimetry (PolSAR) data processing. Once constructed, the BPT is processed by a tree pruning, producing a set of spatio-temporal homogeneous regions, and estimating the polarimetric response within them. It is demonstrated that the proposed technique preserves the PolSAR information in the spatial and the temporal domains without introducing bias nor distortion. Additionally, the evolution of the data in the temporal dimension is also analyzed, and techniques to obtain BPT-based scene change maps are defined. Finally, the proposed techniques are employed to process two real RADARSAT-2 data sets.

This paper discusses the interest of hierarchical region-based
representations of images such as Binary Partition Trees (BPTs) and the usefulness of graph cut to process them. BPTs can be considered as an initial abstraction from the signal in which raw pixels are grouped by similarity to form regions, which are hierarchically structured by inclusion in a tree. They provide multiple resolutions of description and easy access to subsets of regions. Their construction is often based on an iterative region-merging algorithm. Once constructed, BPTs can be used for many applications including filtering, segmentation, classification and object detection. Many processing strategies consist in populating the tree with features of interest for the application and in applying a specific graph cut called pruning. Different graph cut approaches are discussed and analyzed in the context of Polarimetric Synthetic Aperture Radar (PolSAR) images.

This study proposes a system to estimate the depth order of regions belonging to a monocular image sequence. For each frame, the regions are ordered according to their relative depth using information from the previous and following frames. The algorithm estimates occlusions relying on a hierarchical region-based representation of the image by means of a binary tree. This representation is used to define the final depth order partition which is obtained through an energy minimisation process. Finally, to achieve a global and consistent depth ordering, a depth order graph is constructed and used to eliminate contradictory local cues. The system is evaluated and compared with the state-of-the-art figure/ground labelling systems showing very good results.

When humans observe a scene, they are able to perfectly distinguish the different parts composing it. Moreover, humans can easily reconstruct the spatial position of these parts and conceive a consistent structure. The mechanisms involving visual perception have been studied since the beginning of neuroscience but, still today, not all the processes composing it are known.
In usual situations, humans can make use of three different methods to estimate the scene structure. The first one is the so called divergence and it makes use of both eyes. When objects lie in front of the observed at a distance up to hundred meters, subtle differences in the image formation in each eye can be used to determine depth. When objects are not in the field of view of both eyes, other mechanisms should be used. In these cases, both visual cues and prior learned information can be used to determine depth. Even if these mechanisms are less accurate than divergence, humans can almost always infer the correct depth structure when using them. As an example of visual cues, occlusion, perspective or object size provide a lot of information about the structure of the scene. A priori information depends on each observer, but it is normally used subconsciously by humans to detect commonly known regions such as the sky, the ground or different types of objects.
In the last years, since technology has been able to handle the processing burden of vision systems, there has been lots of efforts devoted to design automated scene interpreting systems. In this thesis we address the problem of depth estimation using only one point of view and using only occlusion depth cues. The thesis objective is to detect occlusions present in the scene and combine them with a segmentation system so as to generate a relative depth order depth map for a scene. We explore both static and dynamic situations such as single images, frame inside sequences or full video sequences. In the case where a full image sequence is available, a system exploiting motion information to recover depth structure is also designed. Results are promising and competitive with respect to the state of the art literature, but there is still much room for improvement when compared to human depth perception performance.

In this paper, we propose the use of the Binary
Partition Tree (BPT) as a region-based and multi-scale image
representation to process multidimensional SAR data, with special
emphasis on polarimetric SAR data. We also show that
this approach could be extended to other types of remote
sensing imaging technologies, such as hyperspatial imagery. The
Binary Partition Tree contains a lot of information about the
image structure at different detail levels. At the same time, this
structure represents a convenient vehicle to exploit both the
statistical properties, as well as the geometric properties of the
multidimensional SAR data given by the covariance matrix. The
BPT construction process and its exploitation for PolSAR and
temporal data information estimation is analyzed in this work. In
particular, this work focuses on the speckle noise filtering problem
and the temporal characterization of the image dynamics. Results
with real data are presented to illustrate the capabilities of
the BPT processing approach, specially to maintain the spatial
resolution and the small details of the image.

The microarray data classification is an open and active research field. The development of more accurate algorithms is of great interest and many of the developed techniques can be straightforwardly applied in analyzing different kinds of omics data. In this work, an ensemble learning algorithm is applied within a classification framework that already got good predictive results. Ensemble techniques take individual experts, (i.e. classifiers), to combine them to improve the individual expert results with a voting scheme. In this case, a thinning algorithm is proposed which starts by using all the available experts and removes them one by one focusing on improving the ensemble vote. Two versions of a state of the art ensemble thinning algorithm have been tested and three key elements have been introduced to work with microarray data: the ensemble cohort definition, the nonexpert notion, which defines a set of excluded expert from the thinning process, and a rule to break ties in the thinning process. Experiments have been done on seven public datasets from the Microarray Quality Control study, MAQC. The proposed key elements have shown to be useful for the prediction performance and the studied ensemble technique shown to improve the state of the art results by producing classifiers with better predictions.

This paper introduces a non-linear Polarimetric SAR data filtering approach able to preserve the edges and small details of the data. It is based on exploiting the data locality in both, the spatial and the polarimetric domains, in order to avoid mixing heterogeneous samples of the data. A weighted average is performed over a given window favoring pixel values that are close on both domains. The filtering technique is based on a modified bilateral filtering, which is defined in terms of spatial and polarimetric distances. These distances encapsulate all the knowledge in both domains for an adaptation to the data structure. Finally, the proposed technique is employed to process a real RADARSAT-2 dataset.

In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. The BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. Experimental results demonstrate the good performances of this BPT-based approach.

High throughput data analysis is a challenging problem due to the vast amount of available data. A major concern is to develop algorithms that provide accurate numerical predictions and biologically relevant results. A wide variety of tools exist in the literature using biological knowledge to evaluate analysis results. Only recently, some works have included biological knowledge inside the analysis process improving the prediction results.

As early stage of video processing, we introduce an iter-
ative trajectory merging algorithm that produces a region-
based and hierarchical representation of the video se-
quence, called the Trajectory Binary Partition Tree (BPT).
From this representation, many analysis and graph cut tech-
niques can be used to extract partitions or objects that are
useful in the context of specific applications.
In order to define trajectories and to create a precise
merging algorithm, color and motion cues have to be used.
Both types of informations are very useful to characterize
objects but present strong differences of behavior in the spa-
tial and the temporal dimensions. On the one hand, scenes
and objects are rich in their spatial color distributions, but
these distributions are rather stable over time. Object mo-
tion, on the other hand, presents simple structures and low
spatial variability but may change from frame to frame. The
proposed algorithm takes into account this key difference
and relies on different models and associated metrics to
deal with color and motion information. We show that the
proposed algorithm outperforms existing hierarchical video
segmentation algorithms and provides more stable and pre-
cise regions

This paper proposes a system that relates objects
in an image using occlusion cues and arranges them according
to depth. The system does not rely on a priori knowledge of
the scene structure and focuses on detecting special points,
such as T-junctions and highly convex contours, to infer the
depth relationships between objects in the scene. The system
makes extensive use of the binary partition tree as hierarchical
region-based image representation jointly with a new approach
for candidate T-junction estimation. Since some regions may
not involve T-junctions, occlusion is also detected by examining
convex shapes on region boundaries. Combining T-junctions and
convexity leads to a system which only relies on low level depth
cues and does not rely on semantic information. However, it
shows a similar or better performance with the state-of-the-art
while not assuming any type of scene.
As an extension of the automatic depth ordering system, a
semi-automatic approach is also proposed. If the user provides
the depth order for a subset of regions in the image, the system
is able to easily integrate this user information to the final
depth order for the complete image. For some applications, user
interaction can naturally be integrated, improving the quality of
the automatically generated depth map.

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image-processing tools. This paper proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation relying on the binary partition tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the BPT succeeds in presenting: 1) the decomposition of the image in terms of coherent regions, and 2) the inclusion relations of the regions in the scene. Based on region-merging techniques, the BPT construction is investigated by studying the hyperspectral region models and the associated similarity metrics. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. In this paper, a pruning strategy is proposed and discussed in a classification context. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation.

Multiclass cancer classification is still a challenging task in the field of machine learning. A novel multiclass approach is proposed in this work as a combination of multiple binary classifiers. It is an example of Error Correcting Output Codes algorithms, applying data transmission coding techniques to improve the classification as a combination of binary classifiers. The proposed method combines the One Against All, OAA, approach with a set of classifiers separating each class-pair from the rest, called Pair Against All, PAA. The OAA+PAA approach has been tested on seven publicly available datasets. It has been compared with the common OAA approach and with state of the art alternatives. The obtained results showed how the OAA+PAA algorithm consistently improves the OAA results, unlike other ECOC algorithms presented in the literature.

A general framework for microarray data classification is proposed in this paper. It pro-
duces precise and reliable classifiers through a two-step approach. At first, the original
feature set is enhanced by a new set of features called metagenes. These new features
are obtained through a hierarchical clustering process on the original data. Two different
metagene generation rules have been analyzed, called Treelets clustering and Euclidean
clustering. Metagenes creation is attractive for several reasons: first, they can improve
the classification since they broaden the available feature space and capture the com-
mon behavior of similar genes reducing the residual measurement noise. Furthermore,
by analyzing some of the chosen metagenes for classification with gene set enrichment
analysis algorithms, it is shown how metagenes can summarize the behavior of func-
tionally related probe sets. Additionally, metagenes can point out, still undocumented,
highly discriminant probe sets numerically related to other probes endowed with prior
biological information in order to contribute to the knowledge discovery process.
The second step of the framework is the feature selection which applies the Improved
Sequential Floating Forward Selection algorithm (IFFS) to properly choose a subset from
the available feature set for classification composed of genes and metagenes. Considering
the microarray sample scarcity problem, besides the classical error rate, a reliability
measure is introduced to improve the feature selection process. Different scoring schemes
are studied to choose the best one using both error rate and reliability. The Linear
Discriminant Analysis classifier (LDA) has been used throughout this work, due to its
good characteristics, but the proposed framework can be used with almost any classifier.
The potential of the proposed framework has been evaluated analyzing all the publicly
available datasets offered by the Micro Array Quality Control Study, phase II (MAQC).
The comparative results showed that the proposed framework can compete with a wide
variety of state of the art alternatives and it can obtain the best mean performance
if a particular setup is chosen. A Monte Carlo simulation confirmed that the proposed
framework obtains stable and repeatable results.

Microarray data classification is a challenging problem due to the high number of variables compared to the small number of available samples. An effective methodology to output a precise and reliable classifier is proposed in this work as an improvement of the algorithm in [1]. It considers the sample scarcity problem and the lack of data structure typical of microarrays. Both problem are assessed by a two-step approach applying hierarchical clustering to create new features called metagenes and introducing a novel feature ranking criterion, inside the wrapper feature selection task. The classification ability has been evaluated on 4 publicly available datasets from Micro Array Quality Control study phase II (MAQC) classified by 7 different endpoints. The global results have showed how the proposed approach obtains better prediction accuracy than a wide variety of state of the art alternatives.

Microarray data classification is a challenging prob-
lem due to the high number of variables compared to the
small number of available samples. An effective methodology
to output a precise and reliable classifier is proposed in this
work as an improvement of the algorithm in [1]. It considers the
sample scarcity problem and the lack of data structure typical of
microarrays. Both problem are assessed by a two-step approach
applying hierarchical clustering to create new features called
metagenes and introducing a novel feature ranking criterion,
inside the wrapper feature selection task. The classification ability
has been evaluated on 4 publicly available datasets from
Micro
Array Quality Control study phase II
(MAQC) classified by 7
different endpoints. The global results have showed how the
proposed approach obtains better prediction accuracy than a
wide variety of state of the art alternatives

This paper proposes a system to obtain the depth order of frames in image sequences using motion occlusion cues. The system first computes the forward and backward flows with the previous and next frames and estimates the occluded points. To obtain a region representation of the image, a Binary Partition Tree (BPT) is created for each frame. To estimate occlusion relations in the image, projective flow models are fitted to all regions in the image. The depth order solution is obtained by minimizing over the tree structure a cost function based on occlusion relations and the number of regions. Results show that optical flow algorithms can be used directly to estimate occlusion points. Promising results are obtained combining motion occlusions and region information by means of a BPT. Evaluation is performed comparing current state-of-the-art algorithms on figure/ground assignments, showing that the performance of the proposed system is comparable to current algorithms.

The current increase of spatial as well as spectral
resolutions of modern remote sensing sensors represents a
real opportunity for many prac
tical applications but also
generates important challenges in terms of image processing.
In particular, the spatial correlation between pixels and/or the
spectral correlation between spectral bands of a given pixel
cannot be ignored. The traditional pixel-based representation
of images does not facilitate the handling of these correlations.
In this paper, we discuss the inter
est of a particular hierarchical
region-based representation of images based on binary
partition tree (BPT). This representation approach is very
flexible as it can be applied to any type of image. Here both
optical and radar images will be discussed. Moreover, once the
image representation is computed, it can be used for many
different applications. Filtering, segmentation, and classifica-
tion will be detailed in this paper. In all cases, the interest of the
BPT representation over the classical pixel-based representa-
tion will be highlighted

This paper presents a Polarimetric SAR data speckle filtering
technique, based on a combined filtering in the spatial and polarimetric
domains. It is based on a bilateral filtering employing
distance measures over these domains. These measures
concentrate all the information related to the domain structure
that is needed for an adaptation to the scene morphology.
A weighted average is performed over a given window favoring
closer and similar pixels. As a consequence, an adaptive
filtering is achieved, attaining higher filtering over homogeneous
areas whereas point scatters remain almost unchanged.
Results will be shown over a real RADARSAT-2 data.

In this paper, the processing of temporal PolSAR image
series is addressed through a region-based and multi-scale
data representation, the Binary Partition Tree (BPT). This
structure contains useful information related to the data
structure at different detail levels that may be employed for
different applications. The construction of this structure ans
its exploitation is addressed in this work in the context of the
speckle filtering and data segmentation applications. A new
region model and processing strategy are defined to tackle
with the temporal dimension of the data. Finally, to illustrate
the capabilities of the proposed technique, results are shown
with a real RADARSAT-2 dataset.

In this paper, we propose a system to obtain a depth ordered seg-
mentation of a single image based on low level cues. The algorithm
first constructs a hierarchical, region-based image representation of
the image using a Binary Partition Tree (BPT). During the building
process, T-junction depth cues are detected, along with high convex
boundaries. When the BPT is built, a suitable segmentation is found
and a global depth ordering is found using a probabilistic framework.
Results are compared with state of the art depth ordering and
figure/ground labeling systems. The advantage of the proposed ap-
proach compared to systems based on a training procedure is the
lack of assumptions about the scene content. Moreover, it is shown
that the system outperforms previously low-level cue based systems,
while offering similar results to a priori trained figure/ground label-
ing algorithms

In this paper,we propose the use of binary partition
trees (BPT) to introduce a novel region-based and multi-scale polarimetric
SAR (PolSAR) data representation. The BPT structure
represents homogeneous regions in the data at different detail
levels. The construction process of the BPT is based, firstly, on
a region model able to represent the homogeneous areas, and,
secondly, on a dissimilarity measure in order to identify similar
areas and define the merging sequence. Depending on the final
application, a BPT pruning strategy needs to be introduced. In this
paper, we focus on the application of BPT PolSAR data representation
for speckle noise filtering and data segmentation on the basis
of the Gaussian hypothesis, where the average covariance or coherency
matrices are considered as a region model. We introduce
and quantitatively analyze different dissimilarity measures. In this
case, and with the objective to be sensitive to the complete polarimetric
information under the Gaussian hypothesis, dissimilarity
measures considering the complete covariance or coherency matrices
are employed.When confronted to PolSAR speckle filtering,
two pruning strategies are detailed and evaluated. As presented,
the BPT PolSAR speckle filter defined filters data according to the
complete polarimetric information. As shown, this novel filtering
approach is able to achieve very strong filtering while preserving
the spatial resolution and the polarimetric information. Finally,
the BPT representation structure is employed for high spatial
resolution image segmentation applied to coastline detection. The
analyses detailed in this work are based on simulated, as well as on
real PolSAR data acquired by the ESAR system of DLR and the
RADARSAT-2 system.

This paper proposes a system to depth order regions of a
frame belonging to a monocular image sequence. For a given frame, re-
gions are ordered according to their relative depth using the previous
and following frames. The algorithm estimates occluded and disoccluded
pixels belonging to the central frame. Afterwards, a Binary Partition
Tree (BPT) is constructed to obtain a hierarchical, region based repre-
sentation of the image. The nal depth partition is obtained by means
of energy minimization on the BPT. To achieve a global depth ordering
from local occlusion cues, a depth order graph is constructed and used to
eliminate contradictory local cues. Results of the system are evaluated
and compared with state of the art gure/ground labeling systems on
several datasets, showing promising results.

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, under the title Hyperspectral image representation and Processing with Binary Partition Trees, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the Binary Partition Tree succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Based on region-merging techniques, the construction of BPT is investigated in this work by studying hyperspectral region models and the associated similarity metrics. As a matter of fact, the very high dimensionality and the complexity of the data require the definition of specific region models and similarity measures. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. Accordingly, some pruning techniques are proposed and discussed
according to different applications. This Ph.D is focused in particular on segmentation, object detection and classification of hyperspectral imagery. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation

The work presented here proposes a new Binary Partition Tree pruning strategy aimed at the segmentation of hyperspectral images. The BPT is a region-based representation of images that involves a reduced number of elementary primitives and therefore allows to design a robust and efficient segmentation algorithm. Here, the regions contained in the BPT branches are studied by recursive spectral graph partitioning. The goal is to remove subtrees composed of nodes which are considered to be similar. To this end, affinity matrices on the tree branches are computed using a new distance-based measure depending on canonical correlations relating principal coordinates. Experimental results have demonstrated the good performances of BPT construction and pruning.

This problem discusses here is the hierarchical representation and processing of the hyperspectral imaging. In this framework, Binary Partition Trees (BPTs) are proposed as new hierarchical region-based representation. Based on region merging techniques, the work presented here proposes a strategy for merging hyperspectral regions using a new association measure depending on canonical correlations relating principal coordinates. Once is BPT constructed, this representation can be used for many applications including ltering, segmentation and classi cation.To demonstrate an example of BPT usefulness, a pruning strategy aiming at object detection is discussed. Experimental results demonstrate the good performances of BPT.

This paper discusses hierarchical region-based representation using Binary Partition Tree in the framework of hyperspectral data. Based on region merging techniques, this region-based representation reduces the number of elementary primitives compared to the pixel based representation and allows a more robust filtering, segmentation, classification or information retrieval. The work presented here proposes a strategy for merging hyperspectral regions using a new association measure depending on canonical correlations relating principal coordinates. To demonstrate an example of BPT usefulness, a pruning strategy aiming at object detection is discussed. Experimental results demonstrate the good performances of BPT.